Curious what’s actually useful vs hype.
Wanted to get a real-world temperature check on this. My company in India (B2B SaaS) is being pushed by management to “use AI in our QA process” — classic top-down pressure without much direction.
I’ve personally tried:
- GitHub Copilot for autocompleting test cases (actually useful for boilerplate)
- Cursor for refactoring old Selenium tests (surprisingly decent)
- One of the newer “autonomous testing” tools I won’t name ! complete waste of money
What’s been your experience? I’m specifically interested in whether anyone is using AI for: Test case generation from requirements documents
That seems like the most promising use case to me but I haven’t found a solid workflow yet.

Hi, Context, I’m Based in Pakistan, QA architect at a mid-size logistics company.
Copilot for writing unit tests is legitimately good when the code under test is well-structured. Give it a pure function with clear inputs/outputs and it’ll cover edge cases you’d forget. Give it a tangled service class with 10 dependencies and it produces confident-looking garbage.
The “autonomous testing” tools — I’ve evaluated three of them now. They work okayish for simple happy-path flows but they fail completely when your app has complex auth, dynamic content, or anything stateful.
Welcome to the community
Can you explain what that means substatively? Is it really about complex auth flows or how the tools are configured?
Thanks for your response would love to connect if you want to chat more! I think we could learn useful things from eachother 🙌
Welcome to the community
Welcome to the community